The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Electrochemical Methods for the Analysis of Clinically Relevant Biomolecules
Abstract
Rapid progress in identifying biomarkers that are hallmarks of disease has increased demand for high-performance detection technologies. Implementation of electrochemical methods in clinical analysis may provide an effective answer to the growing need for rapid, specific, inexpensive, and fully automated means of biomarker analysis. This Review summarizes advances from the past 5 years in the development of electrochemical sensors for clinically relevant biomolecules, including small molecules, nucleic acids, and proteins. Various sensing strategies are assessed according to their potential for reaching relevant limits of sensitivity, specificity, and degrees of multiplexing. Furthermore, we address the remaining challenges and opportunities to integrate electrochemical sensing platforms into point-of-care solutions.
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Cites
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Cited by
Device integration of electrochemical biosensors
Wu, J., Liu, H., Chen, W., Ma, B., & Ju, H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00032-w · Cited by 895
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Tyagi, D., Wang, H., Huang, W., Hu, L., Tang, Y., Guo, Z., Ouyang, Z., & Zhang, H. (2020) · Nanoscale · doi:10.1039/c9nr10178k · Cited by 508
Metadata
- Authors
- Labib, M., Sargent, E. H., & Kelley, S. O.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-07-18
- Volume
- 116
- Issue
- 16
- Pages
- 9001-9090
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2502992426
- Cited by
- 976
- References
- 32
- FWCI
- 4.55
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
